Overview

Dataset statistics

Number of variables12
Number of observations2966
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.2 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qtd_invoices and 3 other fieldsHigh correlation
recency is highly overall correlated with qtd_invoicesHigh correlation
qtd_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtd_items is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtd_products is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_unique_basket_size is highly overall correlated with qtd_products and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.41723734)Skewed
returns is highly skewed (γ1 = 51.77229749)Skewed
avg_basket_size is highly skewed (γ1 = 44.6542745)Skewed
customer_id has unique valuesUnique
recency has 34 (1.1%) zerosZeros
returns has 1480 (49.9%) zerosZeros

Reproduction

Analysis started2023-11-07 20:06:45.363535
Analysis finished2023-11-07 20:07:35.269969
Duration49.91 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

UNIQUE 

Distinct2966
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.646
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:35.801401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.25
Q113799.75
median15220.5
Q316769.5
95-th percentile17964.75
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.3683
Coefficient of variation (CV)0.11259302
Kurtosis-1.2062852
Mean15270.646
Median Absolute Deviation (MAD)1487
Skewness0.031911548
Sum45292737
Variance2956227.2
MonotonicityNot monotonic
2023-11-07T17:07:36.536862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2956) 2956
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct2951
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2751.6337
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:37.713964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile230.9525
Q1571.02
median1088.53
Q32310.295
95-th percentile7226.025
Maximum279138.02
Range279131.82
Interquartile range (IQR)1739.275

Descriptive statistics

Standard deviation10585.697
Coefficient of variation (CV)3.847059
Kurtosis353.6074
Mean2751.6337
Median Absolute Deviation (MAD)673.06
Skewness16.769761
Sum8161345.4
Variance1.1205698 × 108
MonotonicityNot monotonic
2023-11-07T17:07:38.323227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2053.02 2
 
0.1%
331 2
 
0.1%
734.94 2
 
0.1%
1025.44 2
 
0.1%
598.2 2
 
0.1%
533.33 2
 
0.1%
731.9 2
 
0.1%
2092.32 2
 
0.1%
379.65 2
 
0.1%
745.06 2
 
0.1%
Other values (2941) 2946
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
70.02 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.192852
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:38.959250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.574664
Coefficient of variation (CV)1.2084627
Kurtosis2.7616799
Mean64.192852
Median Absolute Deviation (MAD)26
Skewness1.7950064
Sum190396
Variance6017.8286
MonotonicityNot monotonic
2023-11-07T17:07:39.665164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
3 85
 
2.9%
2 84
 
2.8%
8 76
 
2.6%
10 67
 
2.3%
7 66
 
2.2%
9 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2217
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 84
2.8%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 3
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qtd_invoices
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7242077
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:40.298309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8551469
Coefficient of variation (CV)1.5469646
Kurtosis191.00754
Mean5.7242077
Median Absolute Deviation (MAD)2
Skewness10.770512
Sum16978
Variance78.413626
MonotonicityNot monotonic
2023-11-07T17:07:40.905424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 785
26.5%
3 497
16.8%
4 394
13.3%
5 236
 
8.0%
1 189
 
6.4%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 68
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 189
 
6.4%
2 785
26.5%
3 497
16.8%
4 394
13.3%
5 236
 
8.0%
6 173
 
5.8%
7 139
 
4.7%
8 98
 
3.3%
9 68
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%

qtd_items
Real number (ℝ)

HIGH CORRELATION 

Distinct1672
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1610.2599
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:41.559288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile103
Q1297
median642
Q31401
95-th percentile4408
Maximum196844
Range196842
Interquartile range (IQR)1104

Descriptive statistics

Standard deviation5890.3815
Coefficient of variation (CV)3.6580315
Kurtosis465.56335
Mean1610.2599
Median Absolute Deviation (MAD)422.5
Skewness17.850458
Sum4776031
Variance34696595
MonotonicityNot monotonic
2023-11-07T17:07:42.237582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
88 9
 
0.3%
150 9
 
0.3%
288 8
 
0.3%
246 8
 
0.3%
272 8
 
0.3%
260 8
 
0.3%
84 8
 
0.3%
114 7
 
0.2%
493 7
 
0.2%
Other values (1662) 2883
97.2%
ValueCountFrequency (%)
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
26 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%

qtd_products
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.81187
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:42.775409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation270.00815
Coefficient of variation (CV)2.1985509
Kurtosis354.60552
Mean122.81187
Median Absolute Deviation (MAD)44
Skewness15.702618
Sum364260
Variance72904.399
MonotonicityNot monotonic
2023-11-07T17:07:43.334854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 37
 
1.2%
29 35
 
1.2%
35 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
25 31
 
1.0%
27 30
 
1.0%
26 30
 
1.0%
Other values (457) 2626
88.5%
ValueCountFrequency (%)
1 5
 
0.2%
2 14
0.5%
3 16
0.5%
4 17
0.6%
5 26
0.9%
6 28
0.9%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2697 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1672 1
< 0.1%
1637 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2964
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.936044
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:43.860599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9143418
Q113.119823
median17.977335
Q324.991714
95-th percentile90.50125
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.871892

Descriptive statistics

Standard deviation1037.4582
Coefficient of variation (CV)19.975687
Kurtosis2887.7878
Mean51.936044
Median Absolute Deviation (MAD)5.9963256
Skewness53.417237
Sum154042.31
Variance1076319.5
MonotonicityNot monotonic
2023-11-07T17:07:44.364567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.47833333 2
 
0.1%
4.162 2
 
0.1%
18.15222222 1
 
< 0.1%
12.949 1
 
< 0.1%
16.29372093 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2954) 2954
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

HIGH CORRELATION 

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.378936
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:44.850416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q126
median48.392857
Q385.333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.333333

Descriptive statistics

Standard deviation63.561155
Coefficient of variation (CV)0.94333867
Kurtosis4.8826514
Mean67.378936
Median Absolute Deviation (MAD)26.27381
Skewness2.06229
Sum199845.92
Variance4040.0205
MonotonicityNot monotonic
2023-11-07T17:07:45.297824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
70 21
 
0.7%
4 21
 
0.7%
7 20
 
0.7%
35 18
 
0.6%
49 18
 
0.6%
46 17
 
0.6%
21 17
 
0.6%
11 17
 
0.6%
28 16
 
0.5%
Other values (1248) 2776
93.6%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 21
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION 

Distinct1349
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06316847
Minimum0.0054495913
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:45.758933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0094339623
Q10.017777778
median0.029296366
Q30.055319294
95-th percentile0.22222222
Maximum3
Range2.9945504
Interquartile range (IQR)0.037541516

Descriptive statistics

Standard deviation0.13440508
Coefficient of variation (CV)2.1277241
Kurtosis121.98493
Mean0.06316847
Median Absolute Deviation (MAD)0.014270062
Skewness8.7943007
Sum187.35768
Variance0.018064725
MonotonicityNot monotonic
2023-11-07T17:07:46.202310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3333333333 21
 
0.7%
0.1666666667 21
 
0.7%
0.02777777778 20
 
0.7%
0.09090909091 19
 
0.6%
0.0625 17
 
0.6%
0.1333333333 16
 
0.5%
0.25 15
 
0.5%
0.02380952381 15
 
0.5%
0.4 15
 
0.5%
0.03571428571 15
 
0.5%
Other values (1339) 2792
94.1%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005509641873 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
3 1
 
< 0.1%
2 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.5 3
 
0.1%
1 14
0.5%
0.8333333333 1
 
< 0.1%
0.75 1
 
< 0.1%
0.6666666667 12
0.4%
0.6514745308 1
 
< 0.1%
0.6 1
 
< 0.1%

returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.124073
Minimum0
Maximum80995
Zeros1480
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:46.643919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1513.2549
Coefficient of variation (CV)24.358591
Kurtosis2762.7859
Mean62.124073
Median Absolute Deviation (MAD)1
Skewness51.772297
Sum184260
Variance2289940.4
MonotonicityNot monotonic
2023-11-07T17:07:47.328572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1480
49.9%
1 164
 
5.5%
2 147
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
7 43
 
1.4%
8 43
 
1.4%
Other values (204) 705
23.8%
ValueCountFrequency (%)
0 1480
49.9%
1 164
 
5.5%
2 147
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.04185
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:47.912903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.291667
Q1103.30833
median172.38095
Q3281.92308
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.61474

Descriptive statistics

Standard deviation791.92626
Coefficient of variation (CV)3.1671748
Kurtosis2253.5421
Mean250.04185
Median Absolute Deviation (MAD)83.083333
Skewness44.654274
Sum741624.13
Variance627147.2
MonotonicityNot monotonic
2023-11-07T17:07:48.713664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
136 8
 
0.3%
60 8
 
0.3%
88 8
 
0.3%
75 8
 
0.3%
197 7
 
0.2%
Other values (1969) 2879
97.1%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

HIGH CORRELATION 

Distinct1006
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.174744
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2023-11-07T17:07:49.154020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.4802632
Q110.020833
median17.2
Q327.75
95-th percentile57
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.729167

Descriptive statistics

Standard deviation19.523034
Coefficient of variation (CV)0.88041754
Kurtosis27.655868
Mean22.174744
Median Absolute Deviation (MAD)8.2
Skewness3.4969867
Sum65770.292
Variance381.14885
MonotonicityNot monotonic
2023-11-07T17:07:49.625389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 53
 
1.8%
14 39
 
1.3%
11 38
 
1.3%
20 33
 
1.1%
9 33
 
1.1%
1 31
 
1.0%
17 31
 
1.0%
18 30
 
1.0%
10 30
 
1.0%
16 29
 
1.0%
Other values (996) 2619
88.3%
ValueCountFrequency (%)
1 31
1.0%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 8
 
0.3%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 24
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
259 1
< 0.1%
203.5 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
127 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%

Interactions

2023-11-07T17:07:30.133986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:45.966134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:49.940053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:53.804064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:57.598297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:01.042259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:04.677936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:08.617105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:12.565079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:16.731914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:20.334253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:25.578124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:30.402271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:46.259505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:50.215453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:54.138370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:57.882674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:01.343473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:04.956327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:08.880216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:12.903447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:17.006784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:20.632417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:26.001463image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:30.651286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:46.633012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:50.775646image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:54.406934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:58.156533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:01.653399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:05.602172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:09.139480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:13.258799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:17.293324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:20.916804image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:26.427196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:30.953637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:47.035087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:51.069531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:54.804054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:58.477566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:01.971568image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:05.900039image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:09.429047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:13.604061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:17.585537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:21.652631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:26.879406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:31.220019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:47.386850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:51.336125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:55.085279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:58.741588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:02.239101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:06.172606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:09.694664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:13.925126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:17.851444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:21.919762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:27.323066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:31.564523image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:47.762654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:51.655735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:55.389044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:59.043558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:02.550312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:06.469418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:10.022455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:14.303768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:18.165441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:22.298303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:27.792893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:31.871607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:48.126951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:51.971543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:55.699464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:59.336170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:02.874609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:06.764804image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:10.378037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:14.679988image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:18.464345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:22.702091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:28.236712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:32.143091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:48.398705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:52.243259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:56.004964image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:59.594294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:03.154735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:07.058645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:10.663861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:15.029603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:18.744462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:23.196162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:28.600619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:32.445461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:48.696591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:52.587054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:56.288994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:59.887307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:03.448500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:07.360917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:10.986379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:15.418467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:19.049383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:23.655553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:28.931622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:32.759057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:48.997735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:52.882245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:56.581465image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:00.177561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:03.753519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:07.643099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:11.405186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:15.799600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:19.346543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:24.083571image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:29.231736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:33.098436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:49.312293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:53.203447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:56.886794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:00.471106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:04.063117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:08.007938image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:11.779501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:16.117135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:19.658640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:24.589834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:29.529662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:33.550931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:49.613890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:53.517868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:06:57.283764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:00.754882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:04.359437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:08.326247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:12.202499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:16.449137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:19.984159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:25.154510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:07:29.846972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-07T17:07:49.937994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
customer_idgross_revenuerecencyqtd_invoicesqtd_itemsqtd_productsavg_ticketavg_recency_daysfrequencyreturnsavg_basket_sizeavg_unique_basket_size
customer_id1.000-0.077-0.0000.026-0.0710.012-0.1300.018-0.008-0.064-0.124-0.008
gross_revenue-0.0771.000-0.4150.7700.9250.7430.246-0.2490.1620.3730.5740.289
recency-0.000-0.4151.000-0.502-0.407-0.4350.0480.108-0.032-0.121-0.097-0.106
qtd_invoices0.0260.770-0.5021.0000.7160.6900.059-0.2600.1500.2950.0990.024
qtd_items-0.0710.925-0.4070.7161.0000.7290.167-0.2290.1470.3450.7290.319
qtd_products0.0120.743-0.4350.6900.7291.000-0.378-0.1670.1020.2430.3820.699
avg_ticket-0.1300.2460.0480.0590.167-0.3781.000-0.1220.0990.1900.187-0.612
avg_recency_days0.018-0.2490.108-0.260-0.229-0.167-0.1221.000-0.962-0.397-0.0790.047
frequency-0.0080.162-0.0320.1500.1470.1020.099-0.9621.0000.3600.058-0.041
returns-0.0640.373-0.1210.2950.3450.2430.190-0.3970.3601.0000.2100.019
avg_basket_size-0.1240.574-0.0970.0990.7290.3820.187-0.0790.0580.2101.0000.446
avg_unique_basket_size-0.0080.289-0.1060.0240.3190.699-0.6120.047-0.0410.0190.4461.000

Missing values

2023-11-07T17:07:34.237441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-07T17:07:34.973905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecencyqtd_invoicesqtd_itemsqtd_productsavg_ticketavg_recency_daysfrequencyreturnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.01733.0297.018.15222235.5000000.48611140.050.9705888.735294
1130473232.5956.09.01390.0171.018.90403527.2500000.04878035.0154.44444419.000000
2125836705.382.015.05028.0232.028.90250023.1875000.04569950.0335.20000015.466667
313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000005.600000
415100876.00333.03.080.03.0292.0000008.6000000.13636422.026.6666671.000000
5152914623.3025.014.02102.0102.045.32647123.2000000.05444129.0150.1428577.285714
6146885630.877.021.03621.0327.017.21978618.3000000.073569399.0172.42857115.571429
7178095411.9116.012.02057.061.088.71983635.7000000.03910641.0171.4166675.083333
81531160767.900.091.038194.02379.025.5434644.1444440.315508474.0419.71428626.142857
9160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714299.571429
customer_idgross_revenuerecencyqtd_invoicesqtd_itemsqtd_productsavg_ticketavg_recency_daysfrequencyreturnsavg_basket_sizeavg_unique_basket_size
5602177271060.2515.01.0645.066.016.0643946.00.2857146.0645.00000066.0
561217232421.522.02.0203.036.011.70888912.00.1538460.0101.50000018.0
561317468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.5
562413596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000083.0
5630148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.5
563412479473.2011.01.0382.030.015.7733334.00.33333334.0382.00000030.0
565514126706.137.03.0508.015.047.0753333.01.00000050.0169.3333335.0
5661135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333145.0
567115060301.848.04.0262.0120.02.5153331.02.0000000.065.50000030.0
569012558269.967.01.0196.011.024.5418186.00.285714196.0196.00000011.0